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publications
Accelerated Discovery of CH4 Uptake Capacity Metal–Organic Frameworks Using Bayesian Optimization
Published in Advanced Theory and Simulations, 2022
We show that Bayesian optimization can discover new MOFs by exploring a tiny fraction of the search space.
Carbon Capture Phenomena in Metal-Organic Frameworks with Neural Network Potentials
Published in PRX Energy, 2023
We develop a new interatomic potential for a carbon capture metal-organic framework and obtain material properties at a fraction of the compute cost.
Higher-order equivariant neural networks for charge density prediction in materials
Published in npj Computational Materials, 2024
We develop one of the first models to predict the charge density of bulk inorganic materials